The field of the invention is nuclear magnetic resonance imaging (MRI) methods and systems. More particularly, the invention relates to MR imaging of the brain.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
Magnetic resonance imaging currently plays an essential role in the diagnosis of stroke, both in distinguishing between hemorrhage and ischemia, and in determining the extent and localization of the lesion. Another important goal for patient management is prognosis. In terms of clinical decision-making regarding therapeutic options, the challenge of providing early prognosis in stroke can be broken down into two parts. First, how likely is the ischemic tissue to infarct in the absence of intervention? This is a problem of predicting tissue outcome. Second, if the ischemic tissue does infarct, how critical will the resulting cognitive and behavioral deficit be? This is problem of predicting clinical outcome. The goal, ultimately, is to improve stroke patient care, and to accomplish this will require the accurate prediction of tissue fate and the means to translate the prediction of tissue fate into one of clinical fate. Tools that accurately estimate both tissue and clinical outcome in the acute setting would dramatically impact patient care. For example, patients identified early on as having a good prognosis can be spared risky therapeutics. Conversely, early identification of poor prognosis will weight heavily in the decision of whether to use thrombolytics that carry a certain amount of risk.
There are many MR imaging techniques used to acquire diagnostic information from the brain. These include contrast enhanced T1-weighted images that brightly reveal regions where the blood-brain barrier is destroyed, T2-weighted fast-spin-echo (FSE) and fluid attenuated inversion-recovery (FLAIR) imaging which show the extent of edema surrounding a damaged region. Two of the most important diagnostic tools, however, are diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) which measure physiological parameters that correlate with tissue health.
Diffusion-weighted imaging (DWI) is a powerful MRI technique for probing microscopic tissue structure. In DWI, a pulse sequence is employed which contains a magnetic field gradient known as a diffusion gradient that sensitizes the MR signal to spin motion. In a DWI pulse sequence the detected MR signal intensity decreases with the speed of water diffusion in a given volume of tissue. The first moment of this diffusion gradient, also known as the “b-value” determines the speed of diffusion to which the image is sensitive. This b-value may be adjusted by either varying the area of the two lobes of the diffusion magnetic field gradient, or by varying the time interval between them. When water motion in the subject is unrestricted, the MR signal intensity at the center of the echo using a spin-echo diffusion-weighted pulse sequence is related to the b-value as follows:
                    A        =                                            S              ⁡                              (                b                )                                                    S              0                                =                      ⅇ                          -              bD                                                          (        1        )            where the “b-value” b=γ2G2δ2(Δ−δ/3). The parameter γ is the gyromagnetic ratio and G is the amplitude of the applied diffusion magnetic field gradients. S(b) is the MR signal magnitude with diffusion weighting b, and S0 is the MR signal magnitude with no diffusion weighting (b=0). The parameter D is the diffusion coefficient of the fluid (in mm2/s), which directly reflects the fluid viscosity where there are no structural restrictions to diffusion of the water. Δ is the time interval between the onsets of the two diffusion gradient lobes and δ is the duration of each gradient lobe. The diffusion coefficient D in equation (1) may be calculated, since b is known and the attenuation A can be measured.
The interpretation of attenuation A becomes complicated when water molecules are restricted in their motion by tissue structures. Different populations of water within a voxel then diffuse, on average, at different rates. One can fit the measured attenuation data with a mono-exponential function, or make an estimate of the signal decay rate using a single b-value, yielding an apparent diffusion coefficient (ADC). The ADC is useful, in detecting cytotoxic edema following a stroke.
Perfusion as related to tissue refers to the exchange of oxygen, water and nutrients between blood and tissue. The measurement of tissue perfusion is important for the functional assessment of organ health. Perfusion weighted images (PWI) which show by their brightness the degree to which tissues are perfused can be used, to assess the health of brain tissues that have been damaged by a stroke. A number of methods have been used to produce perfusion images using magnetic resonance imaging techniques. One technique, as exemplified by U.S. Pat. No. 6,295,465, is to determine the wash-in or wash-out kinetics of contrast agents such as chelated gadolinium. From the acquired NMR data, images are produced which indicate cerebral blood flow (CBF) at each voxel, cerebral blood volume (CBV) at each voxel and mean transit time (MTT) at each voxel. Each of these perfusion indication measurements provides information that is useful in diagnosing tissue health.
Several studies have noted that DWI- and PWI-derived parameter values, such as the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF), vary on a voxel-by-voxel basis within the ischemic territory, and their values have been found to be associated with the likelihood of infarction. However, no single parameter has been shown to be definitively predictive of infarction, suggesting a multiparametric approach.
Models have been created to correlate the DWI and PWI measurements to tissue outcome. One such method is described by Wu et al “Predicting Tissue Outcome In Acute Human Cerebral Ischemia Using Combined Diffusion- and Perfusion-Weighted MR Imaging”, Stroke, 2001; 32:933-942 and is referred to as the generalized linear model (GLM). With this predictive strategy a model is created that relates predicted outcome P (0=normal, 1=infarcted) to the DWI and PWI measurements with the logistic function:
                    P        =                  1                      1            +                          ⅇ                                                -                  α                                +                                  β                  ⁢                                                                          ⁢                  x                                                                                        (        2        )            where:
α=bias or intercept term that provides the base value for P if all the input parameter x are zero,
β=a vector of the coefficients used to weight each DWI and PWI parameter measurement,
x=the respective DWI and PWI parameter measurements at the voxel.
The vector β is derived from training data acquired from previous patients where the outcomes are known. As described in the above-cited publication and in co-pending U.S. patent application Ser. No. 10/182,978 entitled “Method For Evaluating Novel, Stroke Treatments Using A Tissue Risk Map” this includes selecting training regions in follow-up exams of a stroke patient population and manually selecting regions in T2 weighted images that clearly depict infarcted and noninfarcted tissues. The values from these regions in earlier acquired DWI and PWI parameter images from these same patients were used as the input vector x in the training step. The coefficients (β) are calculated using an iterative reweighted least-squares algorithm.